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With the continuous increase in the number of academic researchers, the volume of scientific papers is also increasing rapidly. The challenge of identifying papers with greater potential academic impact from this large pool has received increasing attention. The citation frequency of a paper is often used as an objective indicator to gauge the academic influence of the paper. The task of citation frequency prediction based on historical citation data in previous studies can achieve high accuracy. However, it can only be executed after the paper has been published for a period. The delay is not conducive to timely discovery of papers with high citation frequency. In this paper, we propose a novel method for predicting cited potential of a paper based on the metadata and semantic information, which can predict the cited potential of academic paper instantly once it has been published. Specifically, the semantic information, such as abstract, semantic span and semantic inflection, is extracted to enhance the ability of the prediction model based on machine learning. To prove the effectiveness and rationality of cited potential prediction model, we conduct two experiments to validate the model and find the most effective combination of input information. The empirical experiments show that the prediction accuracy of our proposed model can reach 88% for the instant prediction of citation., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Zhu, Shuhuai. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

This study is applying critical discourse analysis (CDA) to mission statements of civil society organizations (CSOs) in the Kansai area of Japan to examine how those texts may be reflecting their views on migration and impacting their practices related to international students. The analysis shows similarities across CSO types in choosing organizational names, using vague and bureaucratic vocabulary for formulating their mission statements, as well as confusing and contradictory style of some statements and event announcements that may be misleading or discouraging for potential members. At the same time, diversity of vocabulary referring to their target audience demonstrates shifting perspectives of CSOs on international students and more generally, on foreigners living in Japan.

Every word signifies multiple senses. Many studies using comprehension-based measures suggest that polysemes' senses (e.g., "paper" as in "printer paper" or "term paper") share lexical representations, whereas homophones' meanings (e.g., "pen" as in "ballpoint pen" or "pig pen") correspond to distinct lexical representations. Less is known about the lexical representations of polysemes compared to homophones in language production. In this study, speakers named pictures after reading sentence fragments that primed polysemes and homophones either as direct competitors to pictures (i.e., semantic-competitors), or as indirect-competitors to pictures (e.g., polysemous senses of semantic competitors, or homophonous meanings of semantic competitors). Polysemes (e.g., "paper") elicited equal numbers of intrusions to picture names (e.g., "cardboard") compared to in control conditions whether primed as direct competitors ("printer paper") or as indirect-competitors ("term paper"). This contrasted with the finding that homophones (e.g., "pen") elicited more intrusions to picture names (e.g., crayon) compared to in control conditions when primed as direct competitors ("ballpoint pen") than when primed as indirect-competitors ("pig pen"). These results suggest that polysemes, unlike homophones, are stored and retrieved as unified lexical representations.

The Semantic Link Network is a general semantic model for modeling the structure and the evolution of complex systems. Various semantic links play different roles in rendering the semantics of complex system. One of the basic semantic links represents cause-effect relation, which plays an important role in representation and understanding. This paper verifies the role of the Semantic Link Network in representing the core of text by investigating the contribution of cause-effect link to representing the core of scientific papers. Research carries out with the following steps: (1) Two propositions on the contribution of cause-effect link in rendering the core of paper are proposed and verified through a statistical survey, which shows that the sentences on cause-effect links cover about 65% of key words within each paper on average. (2) An algorithm based on syntactic patterns is designed for automatically extracting cause-effect link from scientific papers, which recalls about 70% of manually annotated cause-effect links on average, indicating that the result adapts to the scale of data sets. (3) The effects of cause-effect link on four schemes of incorporating cause-effect link into the existing instances of the Semantic Link Network for enhancing the summarization of scientific papers are investigated. The experiments show that the quality of the summaries is significantly improved, which verifies the role of semantic links. The significance of this research lies in two aspects: (1) it verifies that the Semantic Link Network connects the important concepts to render the core of text; and, (2) it provides an evidence for realizing content services such as summarization, recommendation and question answering based on the Semantic Link Network, and it can inspire relevant research on content computing., Competing Interests: The authors have declared that no competing interests exist.

If you ask me "Will Semantic Web 'ever' happen, in general, and specifically in education?", the best answer I can give you is "I don't know," but I know that today we are still far away from the hopes that I had when I wrote my paper "Education and The Semantic Web" (Devedzic 2004) more than 10 years ago. Much of the things from that paper are today obsolete, but some remain. With the experience gained and lessons learned over time, as well as with many other developments in the general field of Web-based education, it is possible to sketch a much more realistic picture about Semantic Web and its implications for education than it was possible back then. This article attempts to capture that picture by discussing a diagram.

The study of prothrombotic or thrombophilic states have drawn considerable attention during the past two decades. This was the result of the increasing number of thrombotic events, both arterial and venous reported all over the world but especially in the developed countries. This wealth of studies and papers have not always respected the semantical significance of the various terms used, namely prethrombotic state, hypercoagulable state, thrombophilic or prothrombotic state, thrombophilia, susceptibility to thrombosis and procoagulant state. This review is an attempt to adhere to a correct Semantic format in order to avoid confusion and misinterpretations. This is of fundamental importance in order to avoid the wrong attribution of a thrombosis to a hypercoagulable or a prethrombotic state.

The present study investigated the dominant verb-phrase structure and the semantic functions of the modal "must" in research papers. It also determined whether the modal "must" conforms to or deviates from the standard modal usage in American English. A total of 122 graduate research papers were analyzed. The findings revealed that the dominant-verb phrase structure of the modal "must" was must + base form of the verb and Filipino graduate student researchers most favor the obligation/compulsion/advisability function of the modal must. Moreover, the results of the present study substantiated Morales' (2015) linguistic assumption that the obligation function of the modal must has a continuous usage in Philippine English and further confirmed the findings of Gustillo (2011) that the modal "must" in Philippine English maintains its conformity to the standard modal usage in American English.

A core assumption in the current neurosemantic research is that meanings of concrete words (object nouns, action verbs) are at least partially grounded in modality-specific representations implemented by perceptual and motor cortices. Nevertheless, increasing evidence from the multisensory research suggests that extensive portions of what are traditionally considered modality-specific cortices are in fact supramodal in nature – that is, they can process specific information in multiple sensory modalities and in both normal and sensory-deprived individuals. My central argument in this paper is that the amount of data collected within the supramodal paradigm, although certainly not conclusive, is already robust enough to foster reflections about the role of modality-specificity in the semantic brain and to prompt considerations about the possibility of promoting a property-specific and modality-invariant turn in neurosemantics. [ABSTRACT FROM AUTHOR]

Research on language and cognition relies extensively on psycholinguistic datasets or "norms". These datasets contain judgments of lexical properties like concreteness and age of acquisition, and can be used to norm experimental stimuli, discover empirical relationships in the lexicon, and stress-test computational models. However, collecting human judgments at scale is both time-consuming and expensive. This issue of scale is compounded for multi-dimensional norms and those incorporating context. The current work asks whether large language models (LLMs) can be leveraged to augment the creation of large, psycholinguistic datasets in English. I use GPT-4 to collect multiple kinds of semantic judgments (e.g., word similarity, contextualized sensorimotor associations, iconicity) for English words and compare these judgments against the human "gold standard". For each dataset, I find that GPT-4's judgments are positively correlated with human judgments, in some cases rivaling or even exceeding the average inter-annotator agreement displayed by humans. I then identify several ways in which LLM-generated norms differ from human-generated norms systematically. I also perform several "substitution analyses", which demonstrate that replacing human-generated norms with LLM-generated norms in a statistical model does not change the sign of parameter estimates (though in select cases, there are significant changes to their magnitude). I conclude by discussing the considerations and limitations associated with LLM-generated norms in general, including concerns of data contamination, the choice of LLM, external validity, construct validity, and data quality. Additionally, all of GPT-4's judgments (over 30,000 in total) are made available online for further analysis., (© 2024. The Author(s).)

Word affective ratings are important tools in psycholinguistic research, natural language processing, and many other fields. However, even for well-studied languages, such norms are usually limited in scale. To extrapolate affective (i.e., valence and arousal) values for words in the SUBTLEX-CH database (Cai & Brysbaert, 2010, PLoS ONE, 5(6):e10729), we implemented a computational neural network which captured how words' vector-based semantic representations corresponded to the probability densities of their valence and arousal. Based on these probability density functions, we predicted not only a word's affective values, but also their respective degrees of variability that could characterize individual differences in human affective ratings. The resulting estimates of affective values largely converged with human ratings for both valence and arousal, and the estimated degrees of variability also captured important features of the variability in human ratings. We released the extrapolated affective values, together with their corresponding degrees of variability, for over 38,000 Chinese words in the Open Science Framework ( https://osf.io/s9zmd/ ). We also discussed how the view of embodied cognition could be illuminated by this computational model., (© 2023. The Psychonomic Society, Inc.)

Purpose/objectives: This study proposes the utilization of a Natural Language Processing tool to create a semantic search engine for dental education while addressing the increasing concerns of accuracy, bias, and hallucination in outputs generated by AI tools. The paper focuses on developing and evaluating DentQA, a specialized question-answering tool that makes it easy for students to seek information to access information located in handouts or study material distributed by an institution., Methods: DentQA is structured upon the GPT3.5 language model, utilizing prompt engineering to extract information from external dental documents that experts have verified. Evaluation involves non-human metrics (BLEU scores) and human metrics for the tool's performance, relevance, accuracy, and functionality., Results: Non-human metrics confirm DentQA's linguistic proficiency, achieving a Unigram BLEU score of 0.85. Human metrics reveal DentQA's superiority over GPT3.5 in terms of accuracy (p = 0.00004) and absence of hallucination (p = 0.026). Additional metrics confirmed consistent performance across different question types (X 2  (4, N = 200) = 13.0378, p = 0.012). User satisfaction and performance metrics support DentQA's usability and effectiveness, with a response time of 3.5 s and over 70% satisfaction across all evaluated parameters., Conclusions: The study advocates using a semantic search engine in dental education, mitigating concerns of misinformation and hallucination. By outlining the workflow and the utilization of open-source tools and methods, the study encourages the utilization of similar tools for dental education while underscoring the importance of customizing AI models for dentistry. Further optimizations, testing, and utilization of recent advances can contribute to dental education significantly., (© 2024 American Dental Education Association.)

Data on the emotionality of words is important for the selection of experimental stimuli and sentiment analysis on large bodies of text. While norms for valence and arousal have been thoroughly collected in English, most languages do not have access to such large datasets. Moreover, theoretical developments lead to new dimensions being proposed, the norms for which are only partially available. In this paper, we propose a transformer-based neural network architecture for semantic and emotional norms extrapolation that predicts a whole ensemble of norms at once while achieving state-of-the-art correlations with human judgements on each. We improve on the previous approaches with regards to the correlations with human judgments by Δr = 0.1 on average. We precisely discuss the limitations of norm extrapolation as a whole, with a special focus on the introduced model. Further, we propose a unique practical application of our model by proposing a method of stimuli selection which performs unsupervised control by picking words that match in their semantic content. As the proposed model can easily be applied to different languages, we provide norm extrapolations for English, Polish, Dutch, German, French, and Spanish. To aid researchers, we also provide access to the extrapolation networks through an accessible web application., (© 2023. The Author(s).)

Discusses the results of a project to identify the kinds of verbs used in citations in academic papers as a basis for developing teaching materials for non-native-speaker students who need to read or write academic papers. (19 references) (GLR)

The Mayan language Mam uses complex predicates to express events. Complex predicates map multiple semantic elements onto a single word, and consequently have a blend of lexical and phrasal features. The chameleon-like nature of complex predicates provides a window on children's ability to express phrasal combinations at the one-word stage of language development. The ubiquity of complex predicates in the adult language insures that children will produce complex predicates as some of their first words. The verb complex in Mam has obligatory inflections for aspect, person, and to a degree direction. The inflections vary in degree of attachment between syllable segments, affixes, and clitics. Inflections with vowels are phonologically free, while inflections without vowels attach as either syllable segments or affixes. The Mam verb complex requires the addition of a phrasal layer to prosodic models of lexical acquisition. The paper used this extended version of prosodic theory to make five predictions for the acquisition of the verb complex. The paper analyzes production data for three children between 2;0 and 2;8 acquiring the northern variety of Mam spoken in San Ildefonso Ixtahuacán, Guatemala. The children's production data for both the intransitive and transitive verb complexes support all five predictions to some degree. The children produced prefixes more frequently on vowel-initial stems than on consonant-initial stems, and they produced imperative suffixes more frequently than prefixes on consonant-initial stems. The children exhibited developmental differences and produced phrasal contractions that the prosodic theory did not predict. The results underline the need to integrate prosody into models of morphosyntactic development, and highlight the significance of complex predicates for theories of language acquisition.